27 research outputs found
Dissipative Imitation Learning for Discrete Dynamic Output Feedback Control with Sparse Data Sets
Imitation learning enables the synthesis of controllers for complex
objectives and highly uncertain plant models. However, methods to provide
stability guarantees to imitation learned controllers often rely on large
amounts of data and/or known plant models. In this paper, we explore an
input-output (IO) stability approach to dissipative imitation learning, which
achieves stability with sparse data sets and with little known about the plant
model. A closed-loop stable dynamic output feedback controller is learned using
expert data, a coarse IO plant model, and a new constraint to enforce
dissipativity on the learned controller. While the learning objective is
nonconvex, iterative convex overbounding (ICO) and projected gradient descent
(PGD) are explored as methods to successfully learn the controller. This new
imitation learning method is applied to two unknown plants and compared to
traditionally learned dynamic output feedback controller and neural network
controller. With little knowledge of the plant model and a small data set, the
dissipativity constrained learned controller achieves closed loop stability and
successfully mimics the behavior of the expert controller, while other methods
often fail to maintain stability and achieve good performance
Data-driven dissipative verification of LTI systems:Multiple shots of data, QDF supply-rate and application to a planar manipulator
We present a data-driven dissipative verification method for LTI systems based on using multiple input-output data. We assume that the supply-rate functions have a quadratic difference form corresponding to the general dissipativity notion known in the behavioural framework. We validate our approach in a practical example using a two-degree-of-freedom planar manipulator from Quanser, with which we demonstrate the applicability of multiple datasets over one-shot of data recently proposed in the literature
Mixed Voltage Angle and Frequency Droop Control for Transient Stability of Interconnected Microgrids with Loss of PMU Measurements
We consider the problem of guaranteeing transient stability of a network of
interconnected angle droop controlled microgrids, where voltage phase angle
measurements from phasor measurement units (PMUs) may be lost, leading to poor
performance and instability. In this paper, we propose a novel mixed voltage
angle and frequency droop control (MAFD) framework to improve the reliability
of such angle droop controlled microgrid interconnections. In this framework,
when the phase angle measurement is lost at a microgrid, conventional frequency
droop control is temporarily used for primary control in place of angle droop
control. We model the network of interconnected microgrids with the MAFD
architecture as a nonlinear switched system. We then propose a
dissipativity-based distributed secondary control design to guarantee transient
stability of this network under arbitrary switching between angle droop and
frequency droop controllers. We demonstrate the performance of this control
framework by simulation on a test 123-feeder distribution network.Comment: American Control Conference (ACC), 202
On the One-Shot Data-Driven Verification of Dissipativity of LTI Systems with General Quadratic Supply Rate Function
Based on a one-shot input-output set of data from an LTI system, we present a verification method of dissipativity property based on a general quadratic supply-rate function. We show the applicability of our approach for identifying suitable general quadratic supply-rate function in two numerical examples, one regarding the estimation of L_2-gains and one where we verify the dissipativity of a mass-spring-damper system